We are interested in computer vision and machine learning with a focus on 3D scene understanding, parsing, reconstruction, material and motion estimation for autonomous intelligent systems such as self-driving cars or household robots. In particular, we investigate how complex prior knowledge can be incorporated into computer vision algorithms for making them robust to variations in our complex 3D world. You can follow us on
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While ground truth datasets spur innovation, many current datasets for evaluating stereo, optical flow, scene flow and other tasks are restricted in terms of size, complexity, and diversity, making it difficult to train and test on realistic data. For example, we co-authored the Middlebury flow dataset \cite{Baker:IJCV:11...
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While the accuracy of optical flow estimation has increased markedly, a number of problems remain, most notably the treatment of motion and image boundaries, the tracking of fast but small/thin objects, and the computational complexity of current methods.
In \cite{Sun:IJCV:2014}, we comprehensively analyze ...
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While many computer vision problems are formulated as purely bottom-up processes, it is well known that top-down cues play an important role in human perception. But how can we integrate this high-level knowledge into current models? In this project, we investigate this question and propose models for stereo \cite{Guney2015CVPR}, sc...
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Holistic scene understanding is an important prerequisite for many indoor and outdoor applications, including autonomous driving, navigation, indoor and outdoor mapping as well as localization. Given a high-dimension input (e.g., image or video stream), the task is to extract a rich but compact representation that is easily acc...
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We propose a novel model and dataset for 3D scene flow estimation with an application to autonomous driving. Taking advantage of the fact that outdoor scenes often decompose into a small number of independently moving objects, we represent each element in the scene by its rigid motion parameters and...
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Stereo techniques have witnessed tremendous progress over the last decades, yet some aspects of the problem still remain challenging today. Striking examples are reflecting and textureless surfaces which cannot easily be recovered using traditional local regularizers. In this work, we therefore prop...
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Best Paper Award at 3DV 2015!
This paper presents a novel probabilistic foundation for volumetric 3-d reconstruction. We formulate the problem as inference in a Markov random field, which accurately captures the dependencies between the occupancy and appearance of each voxel, given all in...
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Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems